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Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization

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Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization
Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: 1) the noise is multiplied by (rather than added to) the original image; 2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: 1) converting the multiplicative model into an addi...
José M. Bioucas-Dias, Mário A. T. Fi
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TIP
Authors José M. Bioucas-Dias, Mário A. T. Figueiredo
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